Time:Wednesday, 14 May. 14:00-16:00pm
Location:Room K01 Guanghua Building 2
We present several optimization models and algorithms dealing with uncertain, massive and/or structured data. Specifically, we discuss
• Distributionally Robust Optimization Models, where many problems can be efficiently solved when the associated uncertain data possess no priori distributions;
• Near-Optimal Online Linear Programming, where the constraint matrix is revealed column by column along with the objective function and a decision has to be made as soon as a variable arrives;
• Least-squares with Nonconvex Regularization, where a sparse or low-rank solution is sought;
• Alternating Direction Method of Multipliers (ADMM), where an example is given to show that the direct extension of ADMM for three-block convex minimization problems is not necessarily convergent, and possible convergent variants are proposed.